Bayesian spatio-temporal models for stream networks

Spatio-temporal models are widely used in many research areas including ecology. The recent proliferation of the use of in-situ sensors in streams and rivers supports space-time water quality modelling and monitoring in near real-time. A new family of spatio-temporal models is introduced. These mode...

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Bibliographic Details
Published in:Computational statistics & data analysis 2022-06, Vol.170, p.107446, Article 107446
Main Authors: Santos-Fernandez, Edgar, Ver Hoef, Jay M., Peterson, Erin E., McGree, James, Isaak, Daniel J., Mengersen, Kerrie
Format: Article
Language:English
Subjects:
Bayesian model
Branching network
Linear regression
Space-time
Vector autoregression
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Summary:Spatio-temporal models are widely used in many research areas including ecology. The recent proliferation of the use of in-situ sensors in streams and rivers supports space-time water quality modelling and monitoring in near real-time. A new family of spatio-temporal models is introduced. These models incorporate spatial dependence using stream distance while temporal autocorrelation is captured using vector autoregression approaches. Several variations of these novel models are proposed using a Bayesian framework. The results show that our proposed models perform well using spatio-temporal data collected from real stream networks, particularly in terms of out-of-sample RMSPE. This is illustrated considering a case study of water temperature data in the northwestern United States.
ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2022.107446